scholarly journals Optimal Estimate of Global Biome—Specific Parameter Settings to Reconstruct NDVI Time Series with the Harmonic ANalysis of Time Series (HANTS) Method

2021 ◽  
Vol 13 (21) ◽  
pp. 4251
Author(s):  
Jie Zhou ◽  
Li Jia ◽  
Massimo Menenti ◽  
Xuan Liu
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Harmonic Analysis ◽  
Remote Sensing Data ◽  
Atmospheric Conditions ◽  
Reconstruction Performance ◽  
Ndvi Time Series ◽  
Harmonic Components ◽  
Analysis Of Time Series ◽  
Optimal Settings

Terrestrial remote sensing data products retrieved from radiometric measurements in the optical and thermal infrared spectrum such as vegetation spectral indices can be heavily contaminated by atmospheric conditions, including cloud and aerosol layers. This contamination results in gaps or noisy observations. The harmonic analysis of time series (HANTS) has been widely used for time series reconstruction of remote sensing imagery in recent decades. To use HANTS model, a series of parameters, such as number of frequencies (NF), fitting error tolerance (FET), degree of over-determinedness (DoD), and regularization factor (Delta), need to be defined by users. These parameters provide flexibilities, but also make it difficult for non-expert users to determine appropriate settings for specific applications. This study systematically evaluated the reconstruction performance of the model under different parameter setting scenarios by simulating pixel-wise reference and noisy NDVI time series. The results of these numerical experiments were further used to identify optimal settings and improve global NDVI reconstruction performance. The results suggested optimal settings for different areas (local optimization). If a user opts to use unique settings for global reconstruction, the setting NF = 4, FET = 0.05, DoD = 5, and Delta = 0.5 can produce the best performance across all setting scenarios (global optimization). In addition, several internal improvements, such as dynamic weighting scheme, polynomial and inter-annual harmonic components, and ancillary attributes of input data can be used to further improve the performance of reconstruction. With these results, future non-expert users can easily determine appropriate settings of HANTS for specific applications in different regions.

scholarly journals Comparison of Harmonic Analysis of Time Series (HANTS) and Multi-Singular Spectrum Analysis (M-SSA) in Reconstruction of Long-Gap Missing Data in NDVI Time Series

2020 ◽  
Vol 12 (17) ◽  
pp. 2747
Author(s):  
Hamid Reza Ghafarian Malamiri ◽  
Hadi Zare ◽  
Iman Rousta ◽  
Haraldur Olafsson ◽  
Emma Izquierdo Verdiguier ◽  
...  
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Harmonic Analysis ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Vegetation Changes ◽  
Singular Spectrum ◽  
Ndvi Time Series ◽  
Reconstructed Data ◽  
Analysis Of Time Series

Monitoring vegetation changes over time is very important in dry areas such as Iran, given its pronounced drought-prone agricultural system. Vegetation indices derived from remotely sensed satellite imageries are successfully used to monitor vegetation changes at various scales. Atmospheric dust as well as airborne particles, particularly gases and clouds, significantly affect the reflection of energy from the surface, especially in visible, short and infrared wavelengths. This results in imageries with missing data (gaps) and outliers while vegetation change analysis requires integrated and complete time series data. This study investigated the performance of HANTS (Harmonic ANalysis of Time Series) algorithm and (M)-SSA ((Multi-channel) Singular Spectrum Analysis) algorithm in reconstruction of wide-gap of missing data. The time series of Normalized Difference Vegetation Index (NDVI) retrieved from Landsat TM in combination with 250m MODIS NDVI time image products are used to simulate and find periodic components of the NDVI time series from 1986 to 2000 and from 2000 to 2015, respectively. This paper presents the evaluation of the performance of gap filling capability of HANTS and M-SSA by filling artificially created gaps in data using Landsat and MODIS data. The results showed that the RMSEs (Root Mean Square Errors) between the original and reconstructed data in HANTS and M-SSA algorithms were 0.027 and 0.023 NDVI value, respectively. Further, RMSEs among 15 NDVI images extracted from the time series artificially and reconstructed by HANTS and M-SSA algorithms were 0.030 and 0.025 NDVI value, respectively. RMSEs of the original and reconstructed data in HANTS and M-SSA algorithms were 0.10 and 0.04 for time series 6, respectively. The findings of this study present a favorable option for solving the missing data challenge in NDVI time series.

scholarly journals The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy

2019 ◽  
Vol 8 (11) ◽  
pp. 502 ◽  
Author(s):  
Rui Sun ◽  
Shaohui Chen ◽  
Hongbo Su ◽  
Chunrong Mi ◽  
Ning Jin
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Classification Accuracy ◽  
Remote Sensing Data ◽  
Optimal Time ◽  
Time Range ◽  
Fusion Method ◽  
Sensing Data ◽  
Ndvi Time Series ◽  
Crop Classification

Remote sensing data with high spatial and temporal resolutions can help to improve the accuracy of the estimation of crop planting acreage, and contribute to the formulation and management of agricultural policies. Therefore, it is important to determine whether multisource sensors can obtain high spatial and temporal resolution remote sensing data for the target sensor with the help of the spatiotemporal fusion method. In this study, we employed three different sensor datasets to obtain one normalized difference vegetation index (NDVI) time series dataset with a 5.8-m spatial resolution using a spatial and temporal adaptive reflectance fusion model (STARFM). We studied the effectiveness of using multisource remote sensing data to extract crop classifications and analyzed whether the increase in the NDVI time series density could significantly improve the accuracy of the crop classification. The results indicated that multisource sensor data could be used for crop classification after spatiotemporal fusion and that the data source was not limited by the sensor platform. With the increase in the number of NDVI phases, the classification accuracy of the support vector machine (SVM) and the random forest (RF) classifier gradually improved. If the added NDVI phases were not in the optimal time period for wheat recognition, the classification accuracy was not greatly improved. Under the same conditions, the classification accuracy of the RF classifier was higher than that of the SVM. In addition, this study can serve as a good reference for the selection of the optimal time range for base image pairs in the spatiotemporal fusion method for high accuracy mapping of crops, and help avoid excessive data collection and processing.

scholarly journals Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7403
Author(s):  
Pavel P Fil ◽  
Alla Yu Yurova ◽  
Alexey Dobrokhotov ◽  
Daniil Kozlov
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Shallow Groundwater ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Water Infiltration ◽  
Temporary Ponds ◽  
Small Water ◽  
Negative Exponential ◽  
Topographic Depressions

In semi-arid ecoregions of temperate zones, focused snowmelt water infiltration in topographic depressions is a key, but imperfectly understood, groundwater recharge mechanism. Routine monitoring is precluded by the abundance of depressions. We have used remote-sensing data to construct mass balances and estimate volumes of temporary ponds in the Tambov area of Russia. First, small water bodies were automatically recognized in each of a time series of high-resolution Planet Labs images taken in April and May 2021 by object-oriented supervised classification. A training set of water pixels defined in one of the latest images using a small unmanned aerial vehicle enabled high-confidence predictions of water pixels in the earlier images (Cohen’s Κ = 0.99). A digital elevation model was used to estimate the ponds’ water volumes, which decreased with time following a negative exponential equation. The power of the exponent did not systematically depend on the pond size. With adjustment for estimates of daily Penman evaporation, function-based interpolation of the water bodies’ areas and volumes allowed calculation of daily infiltration into the depression beds. The infiltration was maximal (5–40 mm/day) at onset of spring and decreased with time during the study period. Use of the spatially variable infiltration rates improved steady-state shallow groundwater simulations.

scholarly journals Monitoring Changes to Arctic Vegetation and Glaciers at Ny-Ålesund, Svalbard, Based on Time Series Remote Sensing

2021 ◽  
Vol 13 (19) ◽  
pp. 3845
Author(s):  
Guangbo Ren ◽  
Jianbu Wang ◽  
Yunfei Lu ◽  
Peiqiang Wu ◽  
Xiaoqing Lu ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Time Series ◽  
High Latitude ◽  
Global Climate ◽  
Meteorological Data ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
Coastal Regions ◽  
Average Annual Temperature

Climate change has profoundly affected global ecological security. The most vulnerable region on Earth is the high-latitude Arctic. Identifying the changes in vegetation coverage and glaciers in high-latitude Arctic coastal regions is important for understanding the process and impact of global climate change. Ny-Ålesund, the northern-most human settlement, is typical of these coastal regions and was used as a study site. Vegetation and glacier changes over the past 35 years were studied using time series remote sensing data from Landsat 5/7/8 acquired in 1985, 1989, 2000, 2011, 2015 and 2019. Site survey data in 2019, a digital elevation model from 2009 and meteorological data observed from 1985 to 2019 were also used. The vegetation in the Ny-Ålesund coastal zone showed a trend of declining and then increasing, with a breaking point in 2000. However, the area of vegetation with coverage greater than 30% increased over the whole study period, and the wetland moss area also increased, which may be caused by the accelerated melting of glaciers. Human activities were responsible for the decline in vegetation cover around Ny-Ålesund owing to the construction of the town and airport. Even in areas with vegetation coverage of only 13%, there were at least five species of high-latitude plants. The melting rate of five major glaciers in the study area accelerated, and approximately 82% of the reduction in glacier area occurred after 2000. The elevation of the lowest boundary of the five glaciers increased by 50–70 m. The increase in precipitation and the average annual temperature after 2000 explains the changes in both vegetation coverage and glaciers in the study period.

The Vegetation Remote Sensing Phenology of Qinling Mountains Based on the NDVI and the Response of Temperature to it

2014 ◽  
Vol 700 ◽  
pp. 394-399 ◽  
Author(s):  
Xin Ping Ma ◽  
Hong Ying Bai ◽  
Ying Na He ◽  
Shu Heng Li
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Phenology ◽  
Qinling Mountains ◽  
Threshold Method ◽  
Climate Change Research ◽  
Phenological Data ◽  
Growing Period ◽  
Macro Scale ◽  
Ndvi Time Series

The acquisition vegetation phenology information by using time series of satellite data is an important aspect of the application of remote sensing and climate change research . Based on the MODOS NDVI time series of images in 2000-2010, Dynamic threshold method and GIS tools were used to extract the vegetation phenology parameters of Qinling Mountains in 2000-2010 , the accuracy of remote sensing phenology results was verified combined with the measured phenological data, And analyzed the characteristis of phenological variation and the relationship between temperature changes and the phenology of Qinling region,and quantified the extent of temperature change on vegetation phenology in a macro scale. Calculated :the trend of vegetation phenology variation based on the NDVI and the results of phenological data are consistent. Results show that NDVI has good revealed effect on vegetation phenology; From 2000 to 2010,it ahead of 1.8 days at the beginning period of vegetation phenology and late back 1.2 days at the end period ; The start phenology NDVI was generally greater than the late phenology on spatial distribution; The effective temperatures and the temperature in spring, growing period had a maximum influence on NDVI at beginning phenology period,the temperatures in summer and autumn had greater impact on the final NDVI .

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